# POrTAL: Plan-Orchestrated Tree Assembly for Lookahead

arXiv: https://arxiv.org/abs/2512.06002 · HTML: https://mlsanity.com/paper/2512.06002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale](https://mlsanity.com/paper/2204.03514/) (2022)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [QMDP-Net: Deep Learning for Planning under Partial Observability](https://mlsanity.com/paper/1703.06692/) (2017)
- [Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects](https://mlsanity.com/paper/2212.04554/) (2022)
- [Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent](https://mlsanity.com/paper/2410.18242/) (2024)
- [Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty](https://mlsanity.com/paper/2507.06129/) (2025)
- [Uncertainty Expression for Human-Robot Task Communication](https://mlsanity.com/paper/2503.16493/) (2025)
- [Autonomous search of an airborne release in urban environments using informed tree planning](https://mlsanity.com/paper/2109.03542/) (2021)

## Abstract (arXiv)

When tasking robots in partially observable environments, these robots must efficiently and robustly plan to achieve task goals under uncertainty. Although many probabilistic planning algorithms exist for this purpose, these algorithms can be inefficient if executed with the robot's limited computational resources, or may produce policies that take more steps than expected to achieve the goal. We therefore created a new, lightweight, probabilistic planning algorithm, Plan-Orchestrated Tree Assembly for Lookahead (POrTAL), that combines the strengths of two baseline planning algorithms, FF-Replan and POMCP. We demonstrate that POrTAL is an anytime algorithm that generally outperforms these baselines in terms of the final executed plan length given bounded computation time, especially for problems with only moderate levels of uncertainty.